Combined classifier multi-signal modulation identification method based on fractional Fourier transform

Abstract

The invention provides a combined classifier multi-signal modulation identification method based on fractional Fourier transform. The method includes steps: firstly, solving the problem of multi-signal separation by employing an FRFT technology, then realizing accurate classification and identification of a strong signal with the combination of a SVM by employing a pre-trained CNN in time and frequency domains, extracting characteristics including the spectrum kurtosis, a root-mean-square value, an AR characteristic coefficient and a Renyi entropy etc. of a weak signal to realize characteristic fusion, performing dimension reduction on fusion characteristics by employing a principal component analysis (PCA) to improve the system validity, and inputting the characteristics after dimension reduction to the SVM to realize second signal classification and identification and finally realize accurate and rapid identification of the multi-signal modulation mode with low signal to noise ratio.

Description

technical field [0001] The invention relates to a joint classifier signal modulation identification method, in particular to a joint classifier signal modulation identification method based on fractional Fourier transform (FRFT) for separating multiple signals. Background technique [0002] As the electromagnetic environment becomes more and more dense, the problem of simultaneous arrival of multiple signals brings difficulties to the high reliability identification of signal modulation. Therefore, multi-signal separation has become a key problem to be solved before the classification and identification of single-signal modulation methods. [0003] The time-domain detection method used when multiple signals are not aliased in the time domain is based on the principle of the energy detection method. Although the time-domain algorithm has a small amount of calculation, simple implementation and fast detection speed, the outstanding problem is that it is sensitive to noise. It...

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Problems solved by technology

Particle swarm optimization (PSO) can achieve better optimization results for SVM parameter optimization, but the PSO algorithm itself tends to fall into local optimum and the overall iteration speed is slow, which affects the overall recognition effect

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